With its capabilities for code and content creation, and tools like Copilot already transforming the way people work, generative AI is helping organizations to accomplish more with their data and systems than ever before to deliver real value.
At the core of AI-first operations lies data. It isn’t just a strategic advantage anymore – data has become a necessity for carving a path to AI success and securing a lasting competitive edge. Almost all (94%) business leaders recognize the need to invest in data platforms to realize their AI ambitions and scale them across their businesses, yet a staggering 63% still express a lack of complete trust in the data their company uses today.
Strengthening data foundations and equipping employees with the necessary skills to leverage these insights should be a priority for any business hoping to reap the benefits of this technology. However, navigating this path can be daunting.
Leads the Microsoft Data business across Avanade and Accenture.
Prioritizing investment in data platforms
The reality is that many businesses face a vast and fragmented data landscape. Inconsistent information residing in disparate silos renders it unusable for AI, which thrives on clean, unified datasets. Getting clean, well-maintained data is a significant task and investment.
AI-centric transformation isn’t just about the technology, meaning there’s still opportunity to transform operating models with existing IT investments and by reimagining processes, products and services with AI to unlock new business value. But ultimately, leaders must prioritize investments into data platforms if they hope to achieve both near-term and long-term value from AI.
People-centricity is key
Data platforms manage enterprise data in one unified foundation to create a single source of truth. A strong data platform complemented by employees’ understanding of prompt engineering and prompt refinements increases the level of trust in the outputs of AI and will help organizations harness value faster.
Making AI accessible is key to this. Organizations need to place people at the center of their AI journey, equipping the workforce with the skills to access, interpret, and leverage data effectively. This fosters a culture of data-driven decision-making, where insights inform every step of the business process.
Tools like Microsoft Fabric can bridge the gap between human and machine intelligence, facilitating the seamless integration of AI into workflows. By unifying an organization’s data and analytics, such tools become assets for all employees, enabling deeper data analysis, data-driven decision-making, and the automation of mundane tasks. This accelerates the realization of value from generative AI and allows organizations to quickly adopt new innovations.
Data governance is also critical to ensuring data quality, consistency, and security. If data accuracy is questionable or the risk of using AI seems too high, employees will be reluctant to engage with AI initiatives. Business leaders must implement robust guidelines that empower the workforce to trust and confidently leverage their data for AI projects. By fostering a data-centric culture, employees become active participants in the AI journey, contributing their expertise to unlock value.
Leveraging generative AI to clean data
One significant challenge for businesses is that data cleansing and management are often resource intensive. Manual processes involving meticulous checks, error identification, and correction are not only time-consuming but also prone to human error. This can significantly slow down AI development and implementation, especially for businesses dealing with vast and complex datasets.
Generative AI offers a game-changing solution to this bottleneck. By automating the data cleansing process, these tools can significantly reduce the time and resources required to prepare data for AI models. Generative AI algorithms can be trained to identify common data inconsistencies like missing values, formatting errors, and duplications. By analyzing historical data patterns and learning from pre-defined rules, these AI models can flag inconsistencies with high accuracy, freeing up time for human data scientists to focus on higher value, strategic tasks.
Once inconsistencies are identified, generative AI can then suggest potential corrections based on the context of the data. It can continuously learn and as it processes more data and receives feedback from human experts, it becomes increasingly adept at identifying new types of inconsistencies and providing accurate corrections. This ongoing learning ensures that the data quality fed into AI models remains consistently high.
The impact of leveraging generative AI for data cleaning will be far-reaching. Much like Robotic Process Automation (RPA) revolutionized deterministic rule-based manual processes, data management AI assistants will act as co-pilots for data scientists. By accelerating the readiness of the data foundation, they will enable businesses to deploy AI models faster and start reaping the benefits sooner. However, to maximize their downstream competitive advantage and move beyond descriptive analytics towards truly predictive and prescriptive models, careful execution will be crucial.
The future belongs to those who harness the power of data for AI and there has never been a better moment to drive data transformation. Businesses must increase their data platform investments to achieve a unified, reliable data foundation. Only then can they realise their AI aspirations and scale them across their enterprises. This data-centric approach will not only ensure relevance in the rapidly evolving digital landscape but also drive businesses towards a future fueled by intelligent insights and data-driven decision-making.
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